Computational prediction and validation of specific EmbR binding site on PknH

Tuberculosis drug resistance continues to threaten global health but the underline molecular mechanisms are not clear.Ethambutol(EMB),one of the well-known first-line drugs in tuberculosis treatment is,unfortunately,not free from drug resistance problems.Genomic studies have shown that some genetic mutations in Mycobacterium tuberculosis(Mtb)EmbR,and EmbC/A/B genes cause EMB resistance.EmbR-PknH pair controls embC/A/B operon,which encodes EmbC/A/B genes,and EMB interacts with EmbA/B proteins.However,the EmbR binding site on PknH was unknown.We conducted molecular simulation on the EmbR-peptides binding structures and discovered phosphorylated PknH 273-280(N′-HEALS^(P)DPD-C′)makesβstrand with the EmbR FHA domain,asβ-MoRF(MoRF;molecular recognition feature)does at its binding site.Hydrogen bond number analysis also supported the peptides’β-MoRF forming activity at the EmbR FHA domain.Also,we discovered that previously known phosphorylation residues might have their chronological order according to the phosphorylation status.The discovery validated that Mtb PknH 273-280(N′-HEALSDPD-C′)has reliable EmbR binding affinity.This approach is revolutionary in the computer-aided drug discovery field,because it is the first trial to discover the protein-protein interaction site,and find binding partner in nature from this site.

Structure-Based Prediction of Transcription Factor Binding Sites

Transcription Factors（TFs） are a very diverse family of DNA-binding proteins that play essential roles in the regulation of gene expression through binding to specific DNA sequences. They are considered as one of the prime drug targets since mutations and aberrant TF-DNA interactions are implicated in many diseases.Identification of TF-binding sites on a genomic scale represents a critical step in delineating transcription regulatory networks and remains a major goal in genomic annotations. Recent development of experimental high-throughput technologies has provided valuable information about TF-binding sites at genome scale under various physiological and developmental conditions. Computational approaches can provide a cost-effective alternative and complement the experimental methods by using the vast quantities of available sequence or structural information. In this review we focus on structure-based prediction of transcription factor binding sites. In addition to its potential in genomescale predictions, structure-based approaches can help us better understand the TF-DNA interaction mechanisms and the evolution of transcription factors and their target binding sites. The success of structure-based methods also bears a translational impact on targeted drug design in medicine and biotechnology.